Models of complex systems often contain model parame-ters for important rates, probabilities, and initial state val-ues. By varying the parameter values, the system modeler can study the behavior of the system under a wide range of system and environmental assumptions. However, exhaus-tive exploration of the parameter space of a large model is computationally expensive. Design of experiments tech-niques provide information about the degree of sensitivity of output variables to various input parameters. Design of experiments makes it possible to find parameter values that optimize measured outputs of the system by running fewer experiments than required by less rigorous techniques. This paper describes the design of experiments techniques that have been integrated in the Möbius tool.
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